/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    RegOptimizer.java
 *    Copyright (C) 2006-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.functions.supportVector;

import java.io.Serializable;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

import weka.classifiers.functions.SMOreg;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;

/**
 * Base class implementation for learning algorithm of SMOreg
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -L &lt;double&gt;
 *  The epsilon parameter in epsilon-insensitive loss function.
 *  (default 1.0e-3)
 * </pre>
 * 
 * <pre>
 * -W &lt;double&gt;
 *  The random number seed.
 *  (default 1)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)
 * @version $Revision$
 */
public class RegOptimizer implements OptionHandler, Serializable {

    /** for serialization */
    private static final long serialVersionUID = -2198266997254461814L;

    /** loss type **/
    // protected int m_nLossType = EPSILON;

    /** the loss type: L1 */
    // public final static int L1 = 1;
    /** the loss type: L2 */
    // public final static int L2 = 2;
    /** the loss type: HUBER */
    // public final static int HUBER = 3;
    /** the loss type: EPSILON */
    // public final static int EPSILON = 4;
    /** the loss type */
    // public static final Tag[] TAGS_LOSS_TYPE = {
    // new Tag(L2, "L2"),
    // new Tag(L1, "L1"),
    // new Tag(HUBER, "Huber"),
    // new Tag(EPSILON, "EPSILON"),
    // };
    /** alpha and alpha* arrays containing weights for solving dual problem **/
    public double[] m_alpha;
    public double[] m_alphaStar;

    /** offset **/
    protected double m_b;

    /** epsilon of epsilon-insensitive cost function **/
    protected double m_epsilon = 1e-3;

    /** capacity parameter, copied from SMOreg **/
    protected double m_C = 1.0;

    /** class values/desired output vector **/
    protected double[] m_target;

    /** points to data set **/
    protected Instances m_data;

    /** the kernel */
    protected Kernel m_kernel;

    /** index of class variable in data set **/
    protected int m_classIndex = -1;

    /** number of instances in data set **/
    protected int m_nInstances = -1;

    /** random number generator **/
    protected Random m_random;

    /** seed for initializing random number generator **/
    protected int m_nSeed = 1;

    /** set of support vectors, that is, vectors with alpha(*)!=0 **/
    protected SMOset m_supportVectors;

    /** number of kernel evaluations, used for printing statistics only **/
    protected long m_nEvals = 0;

    /** number of kernel cache hits, used for printing statistics only **/
    protected int m_nCacheHits = -1;

    /** weights for linear kernel **/
    protected double[] m_weights;

    /**
     * Variables to hold weight vector in sparse form. (To reduce storage
     * requirements.)
     */
    protected double[] m_sparseWeights;
    protected int[] m_sparseIndices;

    /** flag to indicate whether the model is built yet **/
    protected boolean m_bModelBuilt = false;

    /** parent SMOreg class **/
    protected SMOreg m_SVM = null;

    /**
     * the default constructor
     */
    public RegOptimizer() {
        super();
        m_random = new Random(m_nSeed);
    }

    /**
     * Gets an enumeration describing the available options.
     * 
     * @return an enumeration of all the available options.
     */
    @Override
    public Enumeration<Option> listOptions() {
        Vector<Option> result = new Vector<Option>();

        result.addElement(new Option("\tThe epsilon parameter in epsilon-insensitive loss function.\n" + "\t(default 1.0e-3)", "L", 1, "-L <double>"));

        // result.addElement(new Option(
        // "\tLoss type (L1, L2, Huber, Epsilon insensitive loss)\n",
        // "L", 1, "-L [L1|L2|HUBER|EPSILON]"));

        result.addElement(new Option("\tThe random number seed.\n" + "\t(default 1)", "W", 1, "-W <double>"));

        return result.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -L &lt;double&gt;
     *  The epsilon parameter in epsilon-insensitive loss function.
     *  (default 1.0e-3)
     * </pre>
     * 
     * <pre>
     * -W &lt;double&gt;
     *  The random number seed.
     *  (default 1)
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {
        String tmpStr;

        tmpStr = Utils.getOption('L', options);
        if (tmpStr.length() != 0) {
            setEpsilonParameter(Double.parseDouble(tmpStr));
        } else {
            setEpsilonParameter(1.0e-3);
        }

        /*
         * tmpStr = Utils.getOption('S', options); if (tmpStr.length() != 0)
         * setLossType(new SelectedTag(tmpStr, TAGS_LOSS_TYPE)); else setLossType(new
         * SelectedTag(EPSILON, TAGS_LOSS_TYPE));
         */

        tmpStr = Utils.getOption('W', options);
        if (tmpStr.length() != 0) {
            setSeed(Integer.parseInt(tmpStr));
        } else {
            setSeed(1);
        }
    }

    /**
     * Gets the current settings of the classifier.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    @Override
    public String[] getOptions() {
        Vector<String> result = new Vector<String>();

        result.add("-L");
        result.add("" + getEpsilonParameter());

        result.add("-W");
        result.add("" + getSeed());

        // result.add("-S";
        // result.add((new SelectedTag(m_nLossType,
        // TAGS_LOSS_TYPE)).getSelectedTag().getReadable();

        return result.toArray(new String[result.size()]);
    }

    /**
     * flag to indicate whether the model was built yet
     * 
     * @return true if the model was built
     */
    public boolean modelBuilt() {
        return m_bModelBuilt;
    }

    /**
     * sets the parent SVM
     * 
     * @param value the parent SVM
     */
    public void setSMOReg(SMOreg value) {
        m_SVM = value;
    }

    /**
     * returns the number of kernel evaluations
     * 
     * @return the number of kernel evaluations
     */
    public long getKernelEvaluations() {
        return m_nEvals;
    }

    /**
     * return the number of kernel cache hits
     * 
     * @return the number of hits
     */
    public int getCacheHits() {
        return m_nCacheHits;
    }

    /**
     * initializes the algorithm
     * 
     * @param data the data to work with
     * @throws Exception if m_SVM is null
     */
    protected void init(Instances data) throws Exception {
        if (m_SVM == null) {
            throw new Exception("SVM not initialized in optimizer. Use RegOptimizer.setSVMReg()");
        }
        m_C = m_SVM.getC();
        m_data = data;
        m_classIndex = data.classIndex();
        m_nInstances = data.numInstances();

        // Initialize kernel
        m_kernel = Kernel.makeCopy(m_SVM.getKernel());
        m_kernel.buildKernel(data);

        // init m_target
        m_target = new double[m_nInstances];
        for (int i = 0; i < m_nInstances; i++) {
            m_target[i] = data.instance(i).classValue();
        }

        m_random = new Random(m_nSeed);

        // initialize alpha and alpha* array to all zero
        m_alpha = new double[m_target.length];
        m_alphaStar = new double[m_target.length];

        m_supportVectors = new SMOset(m_nInstances);

        m_b = 0.0;
        m_nEvals = 0;
        m_nCacheHits = -1;
    }

    /**
     * wrap up various variables to save memeory and do some housekeeping after
     * optimization has finished.
     * 
     * @throws Exception if something goes wrong
     */
    protected void wrapUp() throws Exception {
        m_target = null;

        m_nEvals = m_kernel.numEvals();
        m_nCacheHits = m_kernel.numCacheHits();

        if ((m_SVM.getKernel() instanceof PolyKernel) && ((PolyKernel) m_SVM.getKernel()).getExponent() == 1.0) {
            // convert alpha's to weights
            double[] weights = new double[m_data.numAttributes()];
            for (int k = m_supportVectors.getNext(-1); k != -1; k = m_supportVectors.getNext(k)) {
                for (int j = 0; j < weights.length; j++) {
                    if (j != m_classIndex) {
                        weights[j] += (m_alpha[k] - m_alphaStar[k]) * m_data.instance(k).value(j);
                    }
                }
            }
            m_weights = weights;

            // release memory
            m_alpha = null;
            m_alphaStar = null;
            m_kernel = null;

        } else {
            m_kernel.clean();
        }
        m_bModelBuilt = true;
    }

    /**
     * Compute the value of the objective function.
     * 
     * @return the score
     * @throws Exception if something goes wrong
     */
    protected double getScore() throws Exception {
        double res = 0;
        double t = 0, t2 = 0;
        for (int i = 0; i < m_nInstances; i++) {
            for (int j = 0; j < m_nInstances; j++) {
                t += (m_alpha[i] - m_alphaStar[i]) * (m_alpha[j] - m_alphaStar[j]) * m_kernel.eval(i, j, m_data.instance(i));
            }
            // switch(m_nLossType) {
            // case L1:
            // t2 += m_data.instance(i).classValue() * (m_alpha[i] - m_alpha_[i]);
            // break;
            // case L2:
            // t2 += m_data.instance(i).classValue() * (m_alpha[i] - m_alpha_[i]) -
            // (0.5/m_SVM.getC()) * (m_alpha[i]*m_alpha[i] + m_alpha_[i]*m_alpha_[i]);
            // break;
            // case HUBER:
            // t2 += m_data.instance(i).classValue() * (m_alpha[i] - m_alpha_[i]) -
            // (0.5*m_SVM.getEpsilon()/m_SVM.getC()) * (m_alpha[i]*m_alpha[i] +
            // m_alpha_[i]*m_alpha_[i]);
            // break;
            // case EPSILON:
            // t2 += m_data.instance(i).classValue() * (m_alpha[i] - m_alphaStar[i]) -
            // m_epsilon * (m_alpha[i] + m_alphaStar[i]);
            t2 += m_target[i] * (m_alpha[i] - m_alphaStar[i]) - m_epsilon * (m_alpha[i] + m_alphaStar[i]);
            // break;
            // }
        }
        res += -0.5 * t + t2;
        return res;
    }

    /**
     * learn SVM parameters from data. Subclasses should implement something more
     * interesting.
     * 
     * @param data the data to work with
     * @throws Exception always an Exceoption since subclasses must override it
     */
    public void buildClassifier(Instances data) throws Exception {
        throw new Exception("Don't call this directly, use subclass instead");
    }

    /**
     * sets the loss type type to use
     * 
     * @param newLossType the loss type to use
     */
    // public void setLossType(SelectedTag newLossType) {
    // if (newLossType.getTags() == TAGS_LOSS_TYPE) {
    // m_nLossType = newLossType.getSelectedTag().getID();
    // }
    // }

    /**
     * returns the current loss type
     * 
     * @return the loss type
     */
    // public SelectedTag getLossType() {
    // return new SelectedTag(m_nLossType, TAGS_LOSS_TYPE);
    // }

    /**
     * SVMOutput of an instance in the training set, m_data This uses the cache,
     * unlike SVMOutput(Instance)
     * 
     * @param index index of the training instance in m_data
     * @return the SVM output
     * @throws Exception if something goes wrong
     */
    protected double SVMOutput(int index) throws Exception {
        double result = -m_b;
        for (int i = m_supportVectors.getNext(-1); i != -1; i = m_supportVectors.getNext(i)) {
            result += (m_alpha[i] - m_alphaStar[i]) * m_kernel.eval(index, i, m_data.instance(index));
        }
        return result;
    }

    /**
     * 
     * @param inst
     * @return
     * @throws Exception
     */
    public double SVMOutput(Instance inst) throws Exception {

        double result = -m_b;
        // Is the machine linear?
        if (m_weights != null) {
            // Is weight vector stored in sparse format?
            for (int i = 0; i < inst.numValues(); i++) {
                if (inst.index(i) != m_classIndex) {
                    result += m_weights[inst.index(i)] * inst.valueSparse(i);
                }
            }
        } else {
            for (int i = m_supportVectors.getNext(-1); i != -1; i = m_supportVectors.getNext(i)) {
                result += (m_alpha[i] - m_alphaStar[i]) * m_kernel.eval(-1, i, inst);
            }
        }
        return result;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String seedTipText() {
        return "Seed for random number generator.";
    }

    /**
     * Gets the current seed value for the random number generator
     * 
     * @return the seed value
     */
    public int getSeed() {
        return m_nSeed;
    }

    /**
     * Sets the seed value for the random number generator
     * 
     * @param value the seed value
     */
    public void setSeed(int value) {
        m_nSeed = value;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String epsilonParameterTipText() {
        return "The epsilon parameter of the epsilon insensitive loss function.(default 0.001).";
    }

    /**
     * Get the value of epsilon parameter of the epsilon insensitive loss function.
     * 
     * @return Value of epsilon parameter.
     */
    public double getEpsilonParameter() {
        return m_epsilon;
    }

    /**
     * Set the value of epsilon parameter of the epsilon insensitive loss function.
     * 
     * @param v Value to assign to epsilon parameter.
     */
    public void setEpsilonParameter(double v) {
        m_epsilon = v;
    }

    /**
     * Prints out the classifier.
     * 
     * @return a description of the classifier as a string
     */
    @Override
    public String toString() {
        StringBuffer text = new StringBuffer();
        text.append("SMOreg\n\n");
        if (m_weights != null) {
            text.append("weights (not support vectors):\n");
            // it's a linear machine
            for (int i = 0; i < m_data.numAttributes(); i++) {
                if (i != m_classIndex) {
                    text.append((m_weights[i] >= 0 ? " + " : " - ") + Utils.doubleToString(Math.abs(m_weights[i]), 12, 4) + " * ");
                    if (m_SVM.getFilterType().getSelectedTag().getID() == SMOreg.FILTER_STANDARDIZE) {
                        text.append("(standardized) ");
                    } else if (m_SVM.getFilterType().getSelectedTag().getID() == SMOreg.FILTER_NORMALIZE) {
                        text.append("(normalized) ");
                    }
                    text.append(m_data.attribute(i).name() + "\n");
                }
            }
        } else {
            // non linear, print out all supportvectors
            text.append("Support vectors:\n");
            for (int i = 0; i < m_nInstances; i++) {
                if (m_alpha[i] > 0) {
                    text.append("+" + m_alpha[i] + " * k[" + i + "]\n");
                }
                if (m_alphaStar[i] > 0) {
                    text.append("-" + m_alphaStar[i] + " * k[" + i + "]\n");
                }
            }
        }

        text.append((m_b <= 0 ? " + " : " - ") + Utils.doubleToString(Math.abs(m_b), 12, 4) + "\n\n");

        text.append("\n\nNumber of kernel evaluations: " + m_nEvals);
        if (m_nCacheHits >= 0 && m_nEvals > 0) {
            double hitRatio = 1 - m_nEvals * 1.0 / (m_nCacheHits + m_nEvals);
            text.append(" (" + Utils.doubleToString(hitRatio * 100, 7, 3).trim() + "% cached)");
        }

        return text.toString();
    }

}
